aboutsummaryrefslogtreecommitdiff
path: root/sql/hive
Commit message (Collapse)AuthorAgeFilesLines
* [SPARK-18026][SQL] should not always lowercase partition columns of ↵Wenchen Fan2016-10-253-16/+7
| | | | | | | | | | | | | | | | | | | | partition spec in parser ## What changes were proposed in this pull request? Currently we always lowercase the partition columns of partition spec in parser, with the assumption that table partition columns are always lowercased. However, this is not true for data source tables, which are case preserving. It's safe for now because data source tables don't store partition spec in metastore and don't support `ADD PARTITION`, `DROP PARTITION`, `RENAME PARTITION`, but we should make our code future-proof. This PR makes partition spec case preserving at parser, and improve the `PreprocessTableInsertion` analyzer rule to normalize the partition columns in partition spec, w.r.t. the table partition columns. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #15566 from cloud-fan/partition-spec.
* [SPARK-17409][SQL][FOLLOW-UP] Do Not Optimize Query in CTAS More Than Oncegatorsmile2016-10-251-2/+18
| | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? This follow-up PR is for addressing the [comment](https://github.com/apache/spark/pull/15048). We added two test cases based on the suggestion from yhuai . One is a new test case using the `saveAsTable` API to create a data source table. Another is for CTAS on Hive serde table. Note: No need to backport this PR to 2.0. Will submit a new PR to backport the whole fix with new test cases to Spark 2.0 ### How was this patch tested? N/A Author: gatorsmile <gatorsmile@gmail.com> Closes #15459 from gatorsmile/ctasOptimizedTestCases.
* [SPARK-18028][SQL] simplify TableFileCatalogWenchen Fan2016-10-253-10/+42
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Simplify/cleanup TableFileCatalog: 1. pass a `CatalogTable` instead of `databaseName` and `tableName` into `TableFileCatalog`, so that we don't need to fetch table metadata from metastore again 2. In `TableFileCatalog.filterPartitions0`, DO NOT set `PartitioningAwareFileCatalog.BASE_PATH_PARAM`. According to the [classdoc](https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningAwareFileCatalog.scala#L189-L209), the default value of `basePath` already satisfies our need. What's more, if we set this parameter, we may break the case 2 which is metioned in the classdoc. 3. add `equals` and `hashCode` to `TableFileCatalog` 4. add `SessionCatalog.listPartitionsByFilter` which handles case sensitivity. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #15568 from cloud-fan/table-file-catalog.
* [SPARK-17810][SQL] Default spark.sql.warehouse.dir is relative to local FS ↵Sean Owen2016-10-242-3/+6
| | | | | | | | | | | | | | | | but can resolve as HDFS path ## What changes were proposed in this pull request? Always resolve spark.sql.warehouse.dir as a local path, and as relative to working dir not home dir ## How was this patch tested? Existing tests. Author: Sean Owen <sowen@cloudera.com> Closes #15382 from srowen/SPARK-17810.
* [SPARK-18045][SQL][TESTS] Move `HiveDataFrameAnalyticsSuite` to package `sql`jiangxingbo2016-10-231-72/+0
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The testsuite `HiveDataFrameAnalyticsSuite` has nothing to do with HIVE, we should move it to package `sql`. The original test cases in that suite are splited into two existing testsuites: `DataFrameAggregateSuite` tests for the functions and ~~`SQLQuerySuite`~~`SQLQueryTestSuite` tests for the SQL statements. ## How was this patch tested? ~~Modified `SQLQuerySuite` in package `sql`.~~ Add query file for `SQLQueryTestSuite`. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #15582 from jiangxb1987/group-analytics-test.
* [SPARK-18038][SQL] Move output partitioning definition from UnaryNodeExec to ↵Tejas Patil2016-10-233-1/+10
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | its children ## What changes were proposed in this pull request? Jira : https://issues.apache.org/jira/browse/SPARK-18038 This was a suggestion by rxin over one of the dev list discussion : http://apache-spark-developers-list.1001551.n3.nabble.com/Project-not-preserving-child-partitioning-td19417.html His words: >> It would be better (safer) to move the output partitioning definition into each of the operator and remove it from UnaryExecNode. With this PR, following is the output partitioning and ordering for all the impls of `UnaryExecNode`. UnaryExecNode's impl | outputPartitioning | outputOrdering | comment ------------ | ------------- | ------------ | ------------ AppendColumnsExec | child's | Nil | child's ordering can be used AppendColumnsWithObjectExec | child's | Nil | child's ordering can be used BroadcastExchangeExec | BroadcastPartitioning | Nil | - CoalesceExec | UnknownPartitioning | Nil | - CollectLimitExec | SinglePartition | Nil | - DebugExec | child's | Nil | child's ordering can be used DeserializeToObjectExec | child's | Nil | child's ordering can be used ExpandExec | UnknownPartitioning | Nil | - FilterExec | child's | child's | - FlatMapGroupsInRExec | child's | Nil | child's ordering can be used GenerateExec | child's | Nil | need to dig more GlobalLimitExec | child's | child's | - HashAggregateExec | child's | Nil | - InputAdapter | child's | child's | - InsertIntoHiveTable | child's | Nil | terminal node, doesn't need partitioning LocalLimitExec | child's | child's | - MapElementsExec | child's | child's | - MapGroupsExec | child's | Nil | child's ordering can be used MapPartitionsExec | child's | Nil | child's ordering can be used ProjectExec | child's | child's | - SampleExec | child's | Nil | child's ordering can be used ScriptTransformation | child's | Nil | child's ordering can be used SerializeFromObjectExec | child's | Nil | child's ordering can be used ShuffleExchange | custom | Nil | - SortAggregateExec | child's | sort over grouped exprs | - SortExec | child's | custom | - StateStoreRestoreExec | child's | Nil | child's ordering can be used StateStoreSaveExec | child's | Nil | child's ordering can be used SubqueryExec | child's | child's | - TakeOrderedAndProjectExec | SinglePartition | custom | - WholeStageCodegenExec | child's | child's | - WindowExec | child's | child's | - ## How was this patch tested? This does NOT change any existing functionality so relying on existing tests Author: Tejas Patil <tejasp@fb.com> Closes #15575 from tejasapatil/SPARK-18038_UnaryNodeExec_output_partitioning.
* [SPARK-18035][SQL] Introduce performant and memory efficient APIs to create ↵Tejas Patil2016-10-221-8/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ArrayBasedMapData ## What changes were proposed in this pull request? Jira: https://issues.apache.org/jira/browse/SPARK-18035 In HiveInspectors, I saw that converting Java map to Spark's `ArrayBasedMapData` spent quite sometime in buffer copying : https://github.com/apache/spark/blob/master/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala#L658 The reason being `map.toSeq` allocates a new buffer and copies the map entries to it: https://github.com/scala/scala/blob/2.11.x/src/library/scala/collection/MapLike.scala#L323 This copy is not needed as we get rid of it once we extract the key and value arrays. Here is the call trace: ``` org.apache.spark.sql.hive.HiveInspectors$$anonfun$unwrapperFor$41.apply(HiveInspectors.scala:664) scala.collection.AbstractMap.toSeq(Map.scala:59) scala.collection.MapLike$class.toSeq(MapLike.scala:323) scala.collection.AbstractMap.toBuffer(Map.scala:59) scala.collection.MapLike$class.toBuffer(MapLike.scala:326) scala.collection.AbstractTraversable.copyToBuffer(Traversable.scala:104) scala.collection.TraversableOnce$class.copyToBuffer(TraversableOnce.scala:275) scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48) scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104) scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59) scala.collection.AbstractIterable.foreach(Iterable.scala:54) scala.collection.IterableLike$class.foreach(IterableLike.scala:72) scala.collection.AbstractIterator.foreach(Iterator.scala:1336) scala.collection.Iterator$class.foreach(Iterator.scala:893) scala.collection.generic.Growable$$anonfun$$plus$plus$eq$1.apply(Growable.scala:59) scala.collection.generic.Growable$$anonfun$$plus$plus$eq$1.apply(Growable.scala:59) ``` Also, earlier code was populating keys and values arrays separately by iterating twice. The PR avoids double iteration of the map and does it in one iteration. EDIT: During code review, there were several more places in the code which were found to do similar thing. The PR dedupes those instances and introduces convenient APIs which are performant and memory efficient ## Performance gains The number is subjective and depends on how many map columns are accessed in the query and average entries per map. For one the queries that I tried out, I saw 3% CPU savings (end-to-end) for the query. ## How was this patch tested? This does not change the end result produced so relying on existing tests. Author: Tejas Patil <tejasp@fb.com> Closes #15573 from tejasapatil/SPARK-18035_avoid_toSeq.
* [SPARK-17994][SQL] Add back a file status cache for catalog tablesEric Liang2016-10-223-18/+127
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In SPARK-16980, we removed the full in-memory cache of table partitions in favor of loading only needed partitions from the metastore. This greatly improves the initial latency of queries that only read a small fraction of table partitions. However, since the metastore does not store file statistics, we need to discover those from remote storage. With the loss of the in-memory file status cache this has to happen on each query, increasing the latency of repeated queries over the same partitions. The proposal is to add back a per-table cache of partition contents, i.e. Map[Path, Array[FileStatus]]. This cache would be retained per-table, and can be invalidated through refreshTable() and refreshByPath(). Unlike the prior cache, it can be incrementally updated as new partitions are read. ## How was this patch tested? Existing tests and new tests in `HiveTablePerfStatsSuite`. cc mallman Author: Eric Liang <ekl@databricks.com> Author: Michael Allman <michael@videoamp.com> Author: Eric Liang <ekhliang@gmail.com> Closes #15539 from ericl/meta-cache.
* [SPARK-18042][SQL] OutputWriter should expose file path writtenReynold Xin2016-10-213-15/+20
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch adds a new "path" method on OutputWriter that returns the path of the file written by the OutputWriter. This is part of the necessary work to consolidate structured streaming and batch write paths. The batch write path has a nice feature that each data source can define the extension of the files, and allow Spark to specify the staging directory and the prefix for the files. However, in the streaming path we need to collect the list of files written, and there is no interface right now to do that. ## How was this patch tested? N/A - there is no behavior change and this should be covered by existing tests. Author: Reynold Xin <rxin@databricks.com> Closes #15580 from rxin/SPARK-18042.
* [SPARK-18029][SQL] PruneFileSourcePartitions should not change the output of ↵Wenchen Fan2016-10-213-4/+80
| | | | | | | | | | | | | | | | LogicalRelation ## What changes were proposed in this pull request? In `PruneFileSourcePartitions`, we will replace the `LogicalRelation` with a pruned one. However, this replacement may change the output of the `LogicalRelation` if it doesn't have `expectedOutputAttributes`. This PR fixes it. ## How was this patch tested? the new `PruneFileSourcePartitionsSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #15569 from cloud-fan/partition-bug.
* [SPARK-18021][SQL] Refactor file name specification for data sourcesReynold Xin2016-10-204-34/+24
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently each data source OutputWriter is responsible for specifying the entire file name for each file output. This, however, does not make any sense because we rely on file naming schemes for certain behaviors in Spark SQL, e.g. bucket id. The current approach allows individual data sources to break the implementation of bucketing. On the flip side, we also don't want to move file naming entirely out of data sources, because different data sources do want to specify different extensions. This patch divides file name specification into two parts: the first part is a prefix specified by the caller of OutputWriter (in WriteOutput), and the second part is the suffix that can be specified by the OutputWriter itself. Note that a side effect of this change is that now all file based data sources also support bucketing automatically. There are also some other minor cleanups: - Removed the UUID passed through generic Configuration string - Some minor rewrites for better clarity - Renamed "path" in multiple places to "stagingDir", to more accurately reflect its meaning ## How was this patch tested? This should be covered by existing data source tests. Author: Reynold Xin <rxin@databricks.com> Closes #15562 from rxin/SPARK-18021.
* [SPARK-17698][SQL] Join predicates should not contain filter clausesTejas Patil2016-10-201-23/+101
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Jira : https://issues.apache.org/jira/browse/SPARK-17698 `ExtractEquiJoinKeys` is incorrectly using filter predicates as the join condition for joins. `canEvaluate` [0] tries to see if the an `Expression` can be evaluated using output of a given `Plan`. In case of filter predicates (eg. `a.id='1'`), the `Expression` passed for the right hand side (ie. '1' ) is a `Literal` which does not have any attribute references. Thus `expr.references` is an empty set which theoretically is a subset of any set. This leads to `canEvaluate` returning `true` and `a.id='1'` is treated as a join predicate. While this does not lead to incorrect results but in case of bucketed + sorted tables, we might miss out on avoiding un-necessary shuffle + sort. See example below: [0] : https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala#L91 eg. ``` val df = (1 until 10).toDF("id").coalesce(1) hc.sql("DROP TABLE IF EXISTS table1").collect df.write.bucketBy(8, "id").sortBy("id").saveAsTable("table1") hc.sql("DROP TABLE IF EXISTS table2").collect df.write.bucketBy(8, "id").sortBy("id").saveAsTable("table2") sqlContext.sql(""" SELECT a.id, b.id FROM table1 a FULL OUTER JOIN table2 b ON a.id = b.id AND a.id='1' AND b.id='1' """).explain(true) ``` BEFORE: This is doing shuffle + sort over table scan outputs which is not needed as both tables are bucketed and sorted on the same columns and have same number of buckets. This should be a single stage job. ``` SortMergeJoin [id#38, cast(id#38 as double), 1.0], [id#39, 1.0, cast(id#39 as double)], FullOuter :- *Sort [id#38 ASC NULLS FIRST, cast(id#38 as double) ASC NULLS FIRST, 1.0 ASC NULLS FIRST], false, 0 : +- Exchange hashpartitioning(id#38, cast(id#38 as double), 1.0, 200) : +- *FileScan parquet default.table1[id#38] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table1, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int> +- *Sort [id#39 ASC NULLS FIRST, 1.0 ASC NULLS FIRST, cast(id#39 as double) ASC NULLS FIRST], false, 0 +- Exchange hashpartitioning(id#39, 1.0, cast(id#39 as double), 200) +- *FileScan parquet default.table2[id#39] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table2, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int> ``` AFTER : ``` SortMergeJoin [id#32], [id#33], FullOuter, ((cast(id#32 as double) = 1.0) && (cast(id#33 as double) = 1.0)) :- *FileScan parquet default.table1[id#32] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table1, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int> +- *FileScan parquet default.table2[id#33] Batched: true, Format: ParquetFormat, InputPaths: file:spark-warehouse/table2, PartitionFilters: [], PushedFilters: [], ReadSchema: struct<id:int> ``` ## How was this patch tested? - Added a new test case for this scenario : `SPARK-17698 Join predicates should not contain filter clauses` - Ran all the tests in `BucketedReadSuite` Author: Tejas Patil <tejasp@fb.com> Closes #15272 from tejasapatil/SPARK-17698_join_predicate_filter_clause.
* [SPARK-17860][SQL] SHOW COLUMN's database conflict check should respect case ↵Dilip Biswal2016-10-202-23/+2
| | | | | | | | | | | | | | | | | sensitivity configuration ## What changes were proposed in this pull request? SHOW COLUMNS command validates the user supplied database name with database name from qualified table name name to make sure both of them are consistent. This comparison should respect case sensitivity. ## How was this patch tested? Added tests in DDLSuite and existing tests were moved to use new sql based test infrastructure. Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #15423 from dilipbiswal/dkb_show_column_fix.
* [SPARK-17796][SQL] Support wildcard character in filename for LOAD DATA ↵Dongjoon Hyun2016-10-201-0/+30
| | | | | | | | | | | | | | | | | | | | | | | | | LOCAL INPATH ## What changes were proposed in this pull request? Currently, Spark 2.0 raises an `input path does not exist` AnalysisException if the file name contains '*'. It is misleading since it occurs when there exist some matched files. Also, it was a supported feature in Spark 1.6.2. This PR aims to support wildcard characters in filename for `LOAD DATA LOCAL INPATH` SQL command like Spark 1.6.2. **Reported Error Scenario** ```scala scala> sql("CREATE TABLE t(a string)") res0: org.apache.spark.sql.DataFrame = [] scala> sql("LOAD DATA LOCAL INPATH '/tmp/x*' INTO TABLE t") org.apache.spark.sql.AnalysisException: LOAD DATA input path does not exist: /tmp/x*; ``` ## How was this patch tested? Pass the Jenkins test with a new test case. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15376 from dongjoon-hyun/SPARK-17796.
* [SPARK-17980][SQL] Fix refreshByPath for converted Hive tablesEric Liang2016-10-192-3/+20
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? There was a bug introduced in https://github.com/apache/spark/pull/14690 which broke refreshByPath with converted hive tables (though, it turns out it was very difficult to refresh converted hive tables anyways, since you had to specify the exact path of one of the partitions). This changes refreshByPath to invalidate by prefix instead of exact match, and fixes the issue. cc sameeragarwal for refreshByPath changes mallman ## How was this patch tested? Extended unit test. Author: Eric Liang <ekl@databricks.com> Closes #15521 from ericl/fix-caching.
* [SPARK-17974] try 2) Refactor FileCatalog classes to simplify the ↵Eric Liang2016-10-181-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | inheritance tree ## What changes were proposed in this pull request? This renames `BasicFileCatalog => FileCatalog`, combines `SessionFileCatalog` with `PartitioningAwareFileCatalog`, and removes the old `FileCatalog` trait. In summary, ``` MetadataLogFileCatalog extends PartitioningAwareFileCatalog ListingFileCatalog extends PartitioningAwareFileCatalog PartitioningAwareFileCatalog extends FileCatalog TableFileCatalog extends FileCatalog ``` (note that this is a re-submission of https://github.com/apache/spark/pull/15518 which got reverted) ## How was this patch tested? Existing tests Author: Eric Liang <ekl@databricks.com> Closes #15533 from ericl/fix-scalastyle-revert.
* [SPARK-17899][SQL][FOLLOW-UP] debug mode should work for corrupted tableWenchen Fan2016-10-182-10/+17
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Debug mode should work for corrupted table, so that we can really debug ## How was this patch tested? new test in `MetastoreDataSourcesSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #15528 from cloud-fan/debug.
* Revert "[SPARK-17974] Refactor FileCatalog classes to simplify the ↵Reynold Xin2016-10-171-1/+1
| | | | | | inheritance tree" This reverts commit 8daa1a29b65a9b5337518458e9ece1619e8a01e3.
* [SPARK-17974] Refactor FileCatalog classes to simplify the inheritance treeEric Liang2016-10-171-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This renames `BasicFileCatalog => FileCatalog`, combines `SessionFileCatalog` with `PartitioningAwareFileCatalog`, and removes the old `FileCatalog` trait. In summary, ``` MetadataLogFileCatalog extends PartitioningAwareFileCatalog ListingFileCatalog extends PartitioningAwareFileCatalog PartitioningAwareFileCatalog extends FileCatalog TableFileCatalog extends FileCatalog ``` cc cloud-fan mallman ## How was this patch tested? Existing tests Author: Eric Liang <ekl@databricks.com> Closes #15518 from ericl/refactor-session-file-catalog.
* [SPARK-17620][SQL] Determine Serde by hive.default.fileformat when Creating ↵Dilip Biswal2016-10-172-6/+59
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Hive Serde Tables ## What changes were proposed in this pull request? Reopens the closed PR https://github.com/apache/spark/pull/15190 (Please refer to the above link for review comments on the PR) Make sure the hive.default.fileformat is used to when creating the storage format metadata. Output ``` SQL scala> spark.sql("SET hive.default.fileformat=orc") res1: org.apache.spark.sql.DataFrame = [key: string, value: string] scala> spark.sql("CREATE TABLE tmp_default(id INT)") res2: org.apache.spark.sql.DataFrame = [] ``` Before ```SQL scala> spark.sql("DESC FORMATTED tmp_default").collect.foreach(println) .. [# Storage Information,,] [SerDe Library:,org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe,] [InputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcInputFormat,] [OutputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat,] [Compressed:,No,] [Storage Desc Parameters:,,] [ serialization.format,1,] ``` After ```SQL scala> spark.sql("DESC FORMATTED tmp_default").collect.foreach(println) .. [# Storage Information,,] [SerDe Library:,org.apache.hadoop.hive.ql.io.orc.OrcSerde,] [InputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcInputFormat,] [OutputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat,] [Compressed:,No,] [Storage Desc Parameters:,,] [ serialization.format,1,] ``` ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Added new tests to HiveDDLCommandSuite, SQLQuerySuite Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #15495 from dilipbiswal/orc2.
* [SPARK-16980][SQL] Load only catalog table partition metadata required to ↵Michael Allman2016-10-1410-103/+302
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | answer a query (This PR addresses https://issues.apache.org/jira/browse/SPARK-16980.) ## What changes were proposed in this pull request? In a new Spark session, when a partitioned Hive table is converted to use Spark's `HadoopFsRelation` in `HiveMetastoreCatalog`, metadata for every partition of that table are retrieved from the metastore and loaded into driver memory. In addition, every partition's metadata files are read from the filesystem to perform schema inference. If a user queries such a table with predicates which prune that table's partitions, we would like to be able to answer that query without consulting partition metadata which are not involved in the query. When querying a table with a large number of partitions for some data from a small number of partitions (maybe even a single partition), the current conversion strategy is highly inefficient. I suspect this scenario is not uncommon in the wild. In addition to being inefficient in running time, the current strategy is inefficient in its use of driver memory. When the sum of the number of partitions of all tables loaded in a driver reaches a certain level (somewhere in the tens of thousands), their cached data exhaust all driver heap memory in the default configuration. I suspect this scenario is less common (in that not too many deployments work with tables with tens of thousands of partitions), however this does illustrate how large the memory footprint of this metadata can be. With tables with hundreds or thousands of partitions, I would expect the `HiveMetastoreCatalog` table cache to represent a significant portion of the driver's heap space. This PR proposes an alternative approach. Basically, it makes four changes: 1. It adds a new method, `listPartitionsByFilter` to the Catalyst `ExternalCatalog` trait which returns the partition metadata for a given sequence of partition pruning predicates. 1. It refactors the `FileCatalog` type hierarchy to include a new `TableFileCatalog` to efficiently return files only for partitions matching a sequence of partition pruning predicates. 1. It removes partition loading and caching from `HiveMetastoreCatalog`. 1. It adds a new Catalyst optimizer rule, `PruneFileSourcePartitions`, which applies a plan's partition-pruning predicates to prune out unnecessary partition files from a `HadoopFsRelation`'s underlying file catalog. The net effect is that when a query over a partitioned Hive table is planned, the analyzer retrieves the table metadata from `HiveMetastoreCatalog`. As part of this operation, the `HiveMetastoreCatalog` builds a `HadoopFsRelation` with a `TableFileCatalog`. It does not load any partition metadata or scan any files. The optimizer prunes-away unnecessary table partitions by sending the partition-pruning predicates to the relation's `TableFileCatalog `. The `TableFileCatalog` in turn calls the `listPartitionsByFilter` method on its external catalog. This queries the Hive metastore, passing along those filters. As a bonus, performing partition pruning during optimization leads to a more accurate relation size estimate. This, along with c481bdf, can lead to automatic, safe application of the broadcast optimization in a join where it might previously have been omitted. ## Open Issues 1. This PR omits partition metadata caching. I can add this once the overall strategy for the cold path is established, perhaps in a future PR. 1. This PR removes and omits partitioned Hive table schema reconciliation. As a result, it fails to find Parquet schema columns with upper case letters because of the Hive metastore's case-insensitivity. This issue may be fixed by #14750, but that PR appears to have stalled. ericl has contributed to this PR a workaround for Parquet wherein schema reconciliation occurs at query execution time instead of planning. Whether ORC requires a similar patch is an open issue. 1. This PR omits an implementation of `listPartitionsByFilter` for the `InMemoryCatalog`. 1. This PR breaks parquet log output redirection during query execution. I can work around this by running `Class.forName("org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$")` first thing in a Spark shell session, but I haven't figured out how to fix this properly. ## How was this patch tested? The current Spark unit tests were run, and some ad-hoc tests were performed to validate that only the necessary partition metadata is loaded. Author: Michael Allman <michael@videoamp.com> Author: Eric Liang <ekl@databricks.com> Author: Eric Liang <ekhliang@gmail.com> Closes #14690 from mallman/spark-16980-lazy_partition_fetching.
* Revert "[SPARK-17620][SQL] Determine Serde by hive.default.fileformat when ↵Yin Huai2016-10-142-59/+6
| | | | | | Creating Hive Serde Tables" This reverts commit 7ab86244e30ca81eb4fa40ea77b4c2b8881cbab2.
* [SPARK-17620][SQL] Determine Serde by hive.default.fileformat when Creating ↵Dilip Biswal2016-10-142-6/+59
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Hive Serde Tables ## What changes were proposed in this pull request? Make sure the hive.default.fileformat is used to when creating the storage format metadata. Output ``` SQL scala> spark.sql("SET hive.default.fileformat=orc") res1: org.apache.spark.sql.DataFrame = [key: string, value: string] scala> spark.sql("CREATE TABLE tmp_default(id INT)") res2: org.apache.spark.sql.DataFrame = [] ``` Before ```SQL scala> spark.sql("DESC FORMATTED tmp_default").collect.foreach(println) .. [# Storage Information,,] [SerDe Library:,org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe,] [InputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcInputFormat,] [OutputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat,] [Compressed:,No,] [Storage Desc Parameters:,,] [ serialization.format,1,] ``` After ```SQL scala> spark.sql("DESC FORMATTED tmp_default").collect.foreach(println) .. [# Storage Information,,] [SerDe Library:,org.apache.hadoop.hive.ql.io.orc.OrcSerde,] [InputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcInputFormat,] [OutputFormat:,org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat,] [Compressed:,No,] [Storage Desc Parameters:,,] [ serialization.format,1,] ``` ## How was this patch tested? Added new tests to HiveDDLCommandSuite Author: Dilip Biswal <dbiswal@us.ibm.com> Closes #15190 from dilipbiswal/orc.
* [SPARK-17073][SQL][FOLLOWUP] generate column-level statisticswangzhenhua2016-10-141-32/+166
| | | | | | | | | | | | ## What changes were proposed in this pull request? This pr adds some test cases for statistics: case sensitive column names, non ascii column names, refresh table, and also improves some documentation. ## How was this patch tested? add test cases Author: wangzhenhua <wangzhenhua@huawei.com> Closes #15360 from wzhfy/colStats2.
* [SPARK-17903][SQL] MetastoreRelation should talk to external catalog instead ↵Wenchen Fan2016-10-149-32/+45
| | | | | | | | | | | | | | | | of hive client ## What changes were proposed in this pull request? `HiveExternalCatalog` should be the only interface to talk to the hive metastore. In `MetastoreRelation` we can just use `ExternalCatalog` instead of `HiveClient` to interact with hive metastore, and add missing API in `ExternalCatalog`. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #15460 from cloud-fan/relation.
* [SPARK-17827][SQL] maxColLength type should be Int for String and BinaryPete Robbins2016-10-131-1/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? correct the expected type from Length function to be Int ## How was this patch tested? Test runs on little endian and big endian platforms Author: Pete Robbins <robbinspg@gmail.com> Closes #15464 from robbinspg/SPARK-17827.
* [SPARK-17657][SQL] Disallow Users to Change Table Typegatorsmile2016-10-132-0/+37
| | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? Hive allows users to change the table type from `Managed` to `External` or from `External` to `Managed` by altering table's property `EXTERNAL`. See the JIRA: https://issues.apache.org/jira/browse/HIVE-1329 So far, Spark SQL does not correctly support it, although users can do it. Many assumptions are broken in the implementation. Thus, this PR is to disallow users to change it. In addition, we also do not allow users to set the property `EXTERNAL` when creating a table. ### How was this patch tested? Added test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #15230 from gatorsmile/alterTableSetExternal.
* [SPARK-17899][SQL] add a debug mode to keep raw table properties in ↵Wenchen Fan2016-10-132-3/+23
| | | | | | | | | | | | | | | | | | | | HiveExternalCatalog ## What changes were proposed in this pull request? Currently `HiveExternalCatalog` will filter out the Spark SQL internal table properties, e.g. `spark.sql.sources.provider`, `spark.sql.sources.schema`, etc. This is reasonable for external users as they don't want to see these internal properties in `DESC TABLE`. However, as a Spark developer, sometimes we do wanna see the raw table properties. This PR adds a new internal SQL conf, `spark.sql.debug`, to enable debug mode and keep these raw table properties. This config can also be used in similar places where we wanna retain debug information in the future. ## How was this patch tested? new test in MetastoreDataSourcesSuite Author: Wenchen Fan <wenchen@databricks.com> Closes #15458 from cloud-fan/debug.
* [SPARK-17720][SQL] introduce static SQL confWenchen Fan2016-10-115-32/+27
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? SQLConf is session-scoped and mutable. However, we do have the requirement for a static SQL conf, which is global and immutable, e.g. the `schemaStringThreshold` in `HiveExternalCatalog`, the flag to enable/disable hive support, the global temp view database in https://github.com/apache/spark/pull/14897. Actually we've already implemented static SQL conf implicitly via `SparkConf`, this PR just make it explicit and expose it to users, so that they can see the config value via SQL command or `SparkSession.conf`, and forbid users to set/unset static SQL conf. ## How was this patch tested? new tests in SQLConfSuite Author: Wenchen Fan <wenchen@databricks.com> Closes #15295 from cloud-fan/global-conf.
* [SPARK-17338][SQL] add global temp viewWenchen Fan2016-10-107-16/+19
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Global temporary view is a cross-session temporary view, which means it's shared among all sessions. Its lifetime is the lifetime of the Spark application, i.e. it will be automatically dropped when the application terminates. It's tied to a system preserved database `global_temp`(configurable via SparkConf), and we must use the qualified name to refer a global temp view, e.g. SELECT * FROM global_temp.view1. changes for `SessionCatalog`: 1. add a new field `gloabalTempViews: GlobalTempViewManager`, to access the shared global temp views, and the global temp db name. 2. `createDatabase` will fail if users wanna create `global_temp`, which is system preserved. 3. `setCurrentDatabase` will fail if users wanna set `global_temp`, which is system preserved. 4. add `createGlobalTempView`, which is used in `CreateViewCommand` to create global temp views. 5. add `dropGlobalTempView`, which is used in `CatalogImpl` to drop global temp view. 6. add `alterTempViewDefinition`, which is used in `AlterViewAsCommand` to update the view definition for local/global temp views. 7. `renameTable`/`dropTable`/`isTemporaryTable`/`lookupRelation`/`getTempViewOrPermanentTableMetadata`/`refreshTable` will handle global temp views. changes for SQL commands: 1. `CreateViewCommand`/`AlterViewAsCommand` is updated to support global temp views 2. `ShowTablesCommand` outputs a new column `database`, which is used to distinguish global and local temp views. 3. other commands can also handle global temp views if they call `SessionCatalog` APIs which accepts global temp views, e.g. `DropTableCommand`, `AlterTableRenameCommand`, `ShowColumnsCommand`, etc. changes for other public API 1. add a new method `dropGlobalTempView` in `Catalog` 2. `Catalog.findTable` can find global temp view 3. add a new method `createGlobalTempView` in `Dataset` ## How was this patch tested? new tests in `SQLViewSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #14897 from cloud-fan/global-temp-view.
* [SPARK-17741][SQL] Grammar to parse top level and nested data fields separatelyjiangxingbo2016-10-091-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently we use the same rule to parse top level and nested data fields. For example: ``` create table tbl_x( id bigint, nested struct<col1:string,col2:string> ) ``` Shows both syntaxes. In this PR we split this rule in a top-level and nested rule. Before this PR, ``` sql("CREATE TABLE my_tab(column1: INT)") ``` works fine. After this PR, it will throw a `ParseException`: ``` scala> sql("CREATE TABLE my_tab(column1: INT)") org.apache.spark.sql.catalyst.parser.ParseException: no viable alternative at input 'CREATE TABLE my_tab(column1:'(line 1, pos 27) ``` ## How was this patch tested? Add new testcases in `SparkSqlParserSuite`. Author: jiangxingbo <jiangxb1987@gmail.com> Closes #15346 from jiangxb1987/cdt.
* [MINOR][SQL] Use resource path for test_script.shWeiqing Yang2016-10-083-8/+12
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR modified the test case `test("script")` to use resource path for `test_script.sh`. Make the test case portable (even in IntelliJ). ## How was this patch tested? Passed the test case. Before: Run `test("script")` in IntelliJ: ``` Caused by: org.apache.spark.SparkException: Subprocess exited with status 127. Error: bash: src/test/resources/test_script.sh: No such file or directory ``` After: Test passed. Author: Weiqing Yang <yangweiqing001@gmail.com> Closes #15246 from weiqingy/hivetest.
* [SPARK-17761][SQL] Remove MutableRowHerman van Hovell2016-10-075-33/+33
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In practice we cannot guarantee that an `InternalRow` is immutable. This makes the `MutableRow` almost redundant. This PR folds `MutableRow` into `InternalRow`. The code below illustrates the immutability issue with InternalRow: ```scala import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions.GenericMutableRow val struct = new GenericMutableRow(1) val row = InternalRow(struct, 1) println(row) scala> [[null], 1] struct.setInt(0, 42) println(row) scala> [[42], 1] ``` This might be somewhat controversial, so feedback is appreciated. ## How was this patch tested? Existing tests. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #15333 from hvanhovell/SPARK-17761.
* [SPARK-17750][SQL] Fix CREATE VIEW with INTERVAL arithmetic.Dongjoon Hyun2016-10-063-1/+41
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently, Spark raises `RuntimeException` when creating a view with timestamp with INTERVAL arithmetic like the following. The root cause is the arithmetic expression, `TimeAdd`, was transformed into `timeadd` function as a VIEW definition. This PR fixes the SQL definition of `TimeAdd` and `TimeSub` expressions. ```scala scala> sql("CREATE TABLE dates (ts TIMESTAMP)") scala> sql("CREATE VIEW view1 AS SELECT ts + INTERVAL 1 DAY FROM dates") java.lang.RuntimeException: Failed to analyze the canonicalized SQL: ... ``` ## How was this patch tested? Pass Jenkins with a new testcase. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15318 from dongjoon-hyun/SPARK-17750.
* [SPARK-17258][SQL] Parse scientific decimal literals as decimalsHerman van Hovell2016-10-041-1/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Currently Spark SQL parses regular decimal literals (e.g. `10.00`) as decimals and scientific decimal literals (e.g. `10.0e10`) as doubles. The difference between the two confuses most users. This PR unifies the parsing behavior and also parses scientific decimal literals as decimals. This implications in tests are limited to a single Hive compatibility test. ## How was this patch tested? Updated tests in `ExpressionParserSuite` and `SQLQueryTestSuite`. Author: Herman van Hovell <hvanhovell@databricks.com> Closes #14828 from hvanhovell/SPARK-17258.
* [SPARK-17773] Input/Output] Add VoidObjectInspectorErgin Seyfe2016-10-032-0/+3
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Added VoidObjectInspector to the list of PrimitiveObjectInspectors ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Executing following query was failing. select SOME_UDAF*(a.arr) from ( select Array(null) as arr from dim_one_row ) a After the fix, I am getting the correct output: res0: Array[org.apache.spark.sql.Row] = Array([null]) Author: Ergin Seyfe <eseyfe@fb.com> Closes #15337 from seyfe/add_void_object_inspector.
* [SPARK-17073][SQL] generate column-level statisticsZhenhua Wang2016-10-033-35/+113
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Generate basic column statistics for all the atomic types: - numeric types: max, min, num of nulls, ndv (number of distinct values) - date/timestamp types: they are also represented as numbers internally, so they have the same stats as above. - string: avg length, max length, num of nulls, ndv - binary: avg length, max length, num of nulls - boolean: num of nulls, num of trues, num of falsies Also support storing and loading these statistics. One thing to notice: We support analyzing columns independently, e.g.: sql1: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS key;` sql2: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS value;` when running sql2 to collect column stats for `value`, we don’t remove stats of columns `key` which are analyzed in sql1 and not in sql2. As a result, **users need to guarantee consistency** between sql1 and sql2. If the table has been changed before sql2, users should re-analyze column `key` when they want to analyze column `value`: `ANALYZE TABLE src COMPUTE STATISTICS FOR COLUMNS key, value;` ## How was this patch tested? add unit tests Author: Zhenhua Wang <wzh_zju@163.com> Closes #15090 from wzhfy/colStats.
* [SPARK-17509][SQL] When wrapping catalyst datatype to Hive data type avoid…Sital Kedia2016-10-022-177/+145
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? When wrapping catalyst datatypes to Hive data type, wrap function was doing an expensive pattern matching which was consuming around 11% of cpu time. Avoid the pattern matching by returning the wrapper only once and reuse it. ## How was this patch tested? Tested by running the job on cluster and saw around 8% cpu improvements. Author: Sital Kedia <skedia@fb.com> Closes #15064 from sitalkedia/skedia/hive_wrapper.
* [SPARK-17612][SQL] Support `DESCRIBE table PARTITION` SQL syntaxDongjoon Hyun2016-09-291-1/+76
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR implements `DESCRIBE table PARTITION` SQL Syntax again. It was supported until Spark 1.6.2, but was dropped since 2.0.0. **Spark 1.6.2** ```scala scala> sql("CREATE TABLE partitioned_table (a STRING, b INT) PARTITIONED BY (c STRING, d STRING)") res1: org.apache.spark.sql.DataFrame = [result: string] scala> sql("ALTER TABLE partitioned_table ADD PARTITION (c='Us', d=1)") res2: org.apache.spark.sql.DataFrame = [result: string] scala> sql("DESC partitioned_table PARTITION (c='Us', d=1)").show(false) +----------------------------------------------------------------+ |result | +----------------------------------------------------------------+ |a string | |b int | |c string | |d string | | | |# Partition Information | |# col_name data_type comment | | | |c string | |d string | +----------------------------------------------------------------+ ``` **Spark 2.0** - **Before** ```scala scala> sql("CREATE TABLE partitioned_table (a STRING, b INT) PARTITIONED BY (c STRING, d STRING)") res0: org.apache.spark.sql.DataFrame = [] scala> sql("ALTER TABLE partitioned_table ADD PARTITION (c='Us', d=1)") res1: org.apache.spark.sql.DataFrame = [] scala> sql("DESC partitioned_table PARTITION (c='Us', d=1)").show(false) org.apache.spark.sql.catalyst.parser.ParseException: Unsupported SQL statement ``` - **After** ```scala scala> sql("CREATE TABLE partitioned_table (a STRING, b INT) PARTITIONED BY (c STRING, d STRING)") res0: org.apache.spark.sql.DataFrame = [] scala> sql("ALTER TABLE partitioned_table ADD PARTITION (c='Us', d=1)") res1: org.apache.spark.sql.DataFrame = [] scala> sql("DESC partitioned_table PARTITION (c='Us', d=1)").show(false) +-----------------------+---------+-------+ |col_name |data_type|comment| +-----------------------+---------+-------+ |a |string |null | |b |int |null | |c |string |null | |d |string |null | |# Partition Information| | | |# col_name |data_type|comment| |c |string |null | |d |string |null | +-----------------------+---------+-------+ scala> sql("DESC EXTENDED partitioned_table PARTITION (c='Us', d=1)").show(100,false) +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------+ |col_name |data_type|comment| +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------+ |a |string |null | |b |int |null | |c |string |null | |d |string |null | |# Partition Information | | | |# col_name |data_type|comment| |c |string |null | |d |string |null | | | | | |Detailed Partition Information CatalogPartition( Partition Values: [Us, 1] Storage(Location: file:/Users/dhyun/SPARK-17612-DESC-PARTITION/spark-warehouse/partitioned_table/c=Us/d=1, InputFormat: org.apache.hadoop.mapred.TextInputFormat, OutputFormat: org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat, Serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, Properties: [serialization.format=1]) Partition Parameters:{transient_lastDdlTime=1475001066})| | | +-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------+ scala> sql("DESC FORMATTED partitioned_table PARTITION (c='Us', d=1)").show(100,false) +--------------------------------+---------------------------------------------------------------------------------------+-------+ |col_name |data_type |comment| +--------------------------------+---------------------------------------------------------------------------------------+-------+ |a |string |null | |b |int |null | |c |string |null | |d |string |null | |# Partition Information | | | |# col_name |data_type |comment| |c |string |null | |d |string |null | | | | | |# Detailed Partition Information| | | |Partition Value: |[Us, 1] | | |Database: |default | | |Table: |partitioned_table | | |Location: |file:/Users/dhyun/SPARK-17612-DESC-PARTITION/spark-warehouse/partitioned_table/c=Us/d=1| | |Partition Parameters: | | | | transient_lastDdlTime |1475001066 | | | | | | |# Storage Information | | | |SerDe Library: |org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe | | |InputFormat: |org.apache.hadoop.mapred.TextInputFormat | | |OutputFormat: |org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat | | |Compressed: |No | | |Storage Desc Parameters: | | | | serialization.format |1 | | +--------------------------------+---------------------------------------------------------------------------------------+-------+ ``` ## How was this patch tested? Pass the Jenkins tests with a new testcase. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #15168 from dongjoon-hyun/SPARK-17612.
* [SPARK-17666] Ensure that RecordReaders are closed by data source file scansJosh Rosen2016-09-271-1/+5
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch addresses a potential cause of resource leaks in data source file scans. As reported in [SPARK-17666](https://issues.apache.org/jira/browse/SPARK-17666), tasks which do not fully-consume their input may cause file handles / network connections (e.g. S3 connections) to be leaked. Spark's `NewHadoopRDD` uses a TaskContext callback to [close its record readers](https://github.com/apache/spark/blame/master/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala#L208), but the new data source file scans will only close record readers once their iterators are fully-consumed. This patch modifies `RecordReaderIterator` and `HadoopFileLinesReader` to add `close()` methods and modifies all six implementations of `FileFormat.buildReader()` to register TaskContext task completion callbacks to guarantee that cleanup is eventually performed. ## How was this patch tested? Tested manually for now. Author: Josh Rosen <joshrosen@databricks.com> Closes #15245 from JoshRosen/SPARK-17666-close-recordreader.
* [SPARK-17660][SQL] DESC FORMATTED for VIEW Lacks View Definitiongatorsmile2016-09-271-0/+19
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? Before this PR, `DESC FORMATTED` does not have a section for the view definition. We should add it for permanent views, like what Hive does. ``` +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------+-------+ |col_name |data_type |comment| +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------+-------+ |a |int |null | | | | | |# Detailed Table Information| | | |Database: |default | | |Owner: |xiaoli | | |Create Time: |Sat Sep 24 21:46:19 PDT 2016 | | |Last Access Time: |Wed Dec 31 16:00:00 PST 1969 | | |Location: | | | |Table Type: |VIEW | | |Table Parameters: | | | | transient_lastDdlTime |1474778779 | | | | | | |# Storage Information | | | |SerDe Library: |org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe | | |InputFormat: |org.apache.hadoop.mapred.SequenceFileInputFormat | | |OutputFormat: |org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat | | |Compressed: |No | | |Storage Desc Parameters: | | | | serialization.format |1 | | | | | | |# View Information | | | |View Original Text: |SELECT * FROM tbl | | |View Expanded Text: |SELECT `gen_attr_0` AS `a` FROM (SELECT `gen_attr_0` FROM (SELECT `a` AS `gen_attr_0` FROM `default`.`tbl`) AS gen_subquery_0) AS tbl| | +----------------------------+-------------------------------------------------------------------------------------------------------------------------------------+-------+ ``` ### How was this patch tested? Added a test case Author: gatorsmile <gatorsmile@gmail.com> Closes #15234 from gatorsmile/descFormattedView.
* [SPARK-16516][SQL] Support for pushing down filters for decimal and ↵hyukjinkwon2016-09-283-9/+89
| | | | | | | | | | | | | | | | | | | | | | timestamp types in ORC ## What changes were proposed in this pull request? It seems ORC supports all the types in ([`PredicateLeaf.Type`](https://github.com/apache/hive/blob/e085b7e9bd059d91aaf013df0db4d71dca90ec6f/storage-api/src/java/org/apache/hadoop/hive/ql/io/sarg/PredicateLeaf.java#L50-L56)) which includes timestamp type and decimal type. In more details, the types listed in [`SearchArgumentImpl.boxLiteral()`](https://github.com/apache/hive/blob/branch-1.2/ql/src/java/org/apache/hadoop/hive/ql/io/sarg/SearchArgumentImpl.java#L1068-L1093) can be used as a filter value. FYI, inital `case` caluse for supported types was introduced in https://github.com/apache/spark/commit/65d71bd9fbfe6fe1b741c80fed72d6ae3d22b028 and this was not changed overtime. At that time, Hive version was, 0.13 which supports only some types for filter-push down (See [SearchArgumentImpl.java#L945-L965](https://github.com/apache/hive/blob/branch-0.13/ql/src/java/org/apache/hadoop/hive/ql/io/sarg/SearchArgumentImpl.java#L945-L965) at 0.13). However, the version was upgraded into 1.2.x and now it supports more types (See [SearchArgumentImpl.java#L1068-L1093](https://github.com/apache/hive/blob/branch-1.2/ql/src/java/org/apache/hadoop/hive/ql/io/sarg/SearchArgumentImpl.java#L1068-L1093) at 1.2.0) ## How was this patch tested? Unit tests in `OrcFilterSuite` and `OrcQuerySuite` Author: hyukjinkwon <gurwls223@gmail.com> Closes #14172 from HyukjinKwon/SPARK-16516.
* [SPARK-17425][SQL] Override sameResult in HiveTableScanExec to make ↵Yadong Qi2016-09-221-0/+15
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ReuseExchange work in text format table ## What changes were proposed in this pull request? The PR will override the `sameResult` in `HiveTableScanExec` to make `ReuseExchange` work in text format table. ## How was this patch tested? # SQL ```sql SELECT * FROM src t1 JOIN src t2 ON t1.key = t2.key JOIN src t3 ON t1.key = t3.key; ``` # Before ``` == Physical Plan == *BroadcastHashJoin [key#30], [key#34], Inner, BuildRight :- *BroadcastHashJoin [key#30], [key#32], Inner, BuildRight : :- *Filter isnotnull(key#30) : : +- HiveTableScan [key#30, value#31], MetastoreRelation default, src : +- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint))) : +- *Filter isnotnull(key#32) : +- HiveTableScan [key#32, value#33], MetastoreRelation default, src +- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint))) +- *Filter isnotnull(key#34) +- HiveTableScan [key#34, value#35], MetastoreRelation default, src ``` # After ``` == Physical Plan == *BroadcastHashJoin [key#2], [key#6], Inner, BuildRight :- *BroadcastHashJoin [key#2], [key#4], Inner, BuildRight : :- *Filter isnotnull(key#2) : : +- HiveTableScan [key#2, value#3], MetastoreRelation default, src : +- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint))) : +- *Filter isnotnull(key#4) : +- HiveTableScan [key#4, value#5], MetastoreRelation default, src +- ReusedExchange [key#6, value#7], BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint))) ``` cc: davies cloud-fan Author: Yadong Qi <qiyadong2010@gmail.com> Closes #14988 from watermen/SPARK-17425.
* [SPARK-17609][SQL] SessionCatalog.tableExists should not check temp viewWenchen Fan2016-09-222-3/+3
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? After #15054 , there is no place in Spark SQL that need `SessionCatalog.tableExists` to check temp views, so this PR makes `SessionCatalog.tableExists` only check permanent table/view and removes some hacks. This PR also improves the `getTempViewOrPermanentTableMetadata` that is introduced in #15054 , to make the code simpler. ## How was this patch tested? existing tests Author: Wenchen Fan <wenchen@databricks.com> Closes #15160 from cloud-fan/exists.
* [SPARK-17051][SQL] we should use hadoopConf in InsertIntoHiveTableWenchen Fan2016-09-202-10/+31
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Hive confs in hive-site.xml will be loaded in `hadoopConf`, so we should use `hadoopConf` in `InsertIntoHiveTable` instead of `SessionState.conf` ## How was this patch tested? N/A Author: Wenchen Fan <wenchen@databricks.com> Closes #14634 from cloud-fan/bug.
* [SPARK-17502][SQL] Fix Multiple Bugs in DDL Statements on Temporary Viewsgatorsmile2016-09-203-32/+54
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? - When the permanent tables/views do not exist but the temporary view exists, the expected error should be `NoSuchTableException` for partition-related ALTER TABLE commands. However, it always reports a confusing error message. For example, ``` Partition spec is invalid. The spec (a, b) must match the partition spec () defined in table '`testview`'; ``` - When the permanent tables/views do not exist but the temporary view exists, the expected error should be `NoSuchTableException` for `ALTER TABLE ... UNSET TBLPROPERTIES`. However, it reports a missing table property. For example, ``` Attempted to unset non-existent property 'p' in table '`testView`'; ``` - When `ANALYZE TABLE` is called on a view or a temporary view, we should issue an error message. However, it reports a strange error: ``` ANALYZE TABLE is not supported for Project ``` - When inserting into a temporary view that is generated from `Range`, we will get the following error message: ``` assertion failed: No plan for 'InsertIntoTable Range (0, 10, step=1, splits=Some(1)), false, false +- Project [1 AS 1#20] +- OneRowRelation$ ``` This PR is to fix the above four issues. ### How was this patch tested? Added multiple test cases Author: gatorsmile <gatorsmile@gmail.com> Closes #15054 from gatorsmile/tempViewDDL.
* [SPARK-17541][SQL] fix some DDL bugs about table management when same-name ↵Wenchen Fan2016-09-182-11/+12
| | | | | | | | | | | | | | | | | | | | | | temp view exists ## What changes were proposed in this pull request? In `SessionCatalog`, we have several operations(`tableExists`, `dropTable`, `loopupRelation`, etc) that handle both temp views and metastore tables/views. This brings some bugs to DDL commands that want to handle temp view only or metastore table/view only. These bugs are: 1. `CREATE TABLE USING` will fail if a same-name temp view exists 2. `Catalog.dropTempView`will un-cache and drop metastore table if a same-name table exists 3. `saveAsTable` will fail or have unexpected behaviour if a same-name temp view exists. These bug fixes are pulled out from https://github.com/apache/spark/pull/14962 and targets both master and 2.0 branch ## How was this patch tested? new regression tests Author: Wenchen Fan <wenchen@databricks.com> Closes #15099 from cloud-fan/fix-view.
* [SPARK-17518][SQL] Block Users to Specify the Internal Data Source Provider Hivegatorsmile2016-09-182-1/+52
| | | | | | | | | | | | ### What changes were proposed in this pull request? In Spark 2.1, we introduced a new internal provider `hive` for telling Hive serde tables from data source tables. This PR is to block users to specify this in `DataFrameWriter` and SQL APIs. ### How was this patch tested? Added a test case Author: gatorsmile <gatorsmile@gmail.com> Closes #15073 from gatorsmile/formatHive.
* [SPARK-17480][SQL][FOLLOWUP] Fix more instances which calls List.length/size ↵hyukjinkwon2016-09-174-6/+12
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | which is O(n) ## What changes were proposed in this pull request? This PR fixes all the instances which was fixed in the previous PR. To make sure, I manually debugged and also checked the Scala source. `length` in [LinearSeqOptimized.scala#L49-L57](https://github.com/scala/scala/blob/2.11.x/src/library/scala/collection/LinearSeqOptimized.scala#L49-L57) is O(n). Also, `size` calls `length` via [SeqLike.scala#L106](https://github.com/scala/scala/blob/2.11.x/src/library/scala/collection/SeqLike.scala#L106). For debugging, I have created these as below: ```scala ArrayBuffer(1, 2, 3) Array(1, 2, 3) List(1, 2, 3) Seq(1, 2, 3) ``` and then called `size` and `length` for each to debug. ## How was this patch tested? I ran the bash as below on Mac ```bash find . -name *.scala -type f -exec grep -il "while (.*\\.length)" {} \; | grep "src/main" find . -name *.scala -type f -exec grep -il "while (.*\\.size)" {} \; | grep "src/main" ``` and then checked each. Author: hyukjinkwon <gurwls223@gmail.com> Closes #15093 from HyukjinKwon/SPARK-17480-followup.
* [SPARK-17440][SPARK-17441] Fixed Multiple Bugs in ALTER TABLEgatorsmile2016-09-151-30/+37
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? For the following `ALTER TABLE` DDL, we should issue an exception when the target table is a `VIEW`: ```SQL ALTER TABLE viewName SET LOCATION '/path/to/your/lovely/heart' ALTER TABLE viewName SET SERDE 'whatever' ALTER TABLE viewName SET SERDEPROPERTIES ('x' = 'y') ALTER TABLE viewName PARTITION (a=1, b=2) SET SERDEPROPERTIES ('x' = 'y') ALTER TABLE viewName ADD IF NOT EXISTS PARTITION (a='4', b='8') ALTER TABLE viewName DROP IF EXISTS PARTITION (a='2') ALTER TABLE viewName RECOVER PARTITIONS ALTER TABLE viewName PARTITION (a='1', b='q') RENAME TO PARTITION (a='100', b='p') ``` In addition, `ALTER TABLE RENAME PARTITION` is unable to handle data source tables, just like the other `ALTER PARTITION` commands. We should issue an exception instead. ### How was this patch tested? Added a few test cases. Author: gatorsmile <gatorsmile@gmail.com> Closes #15004 from gatorsmile/altertable.
generated by cgit v1.2.3 (git 2.39.1) at 2024-09-18 22:01:50 +0000